Memristors for the Curious Outsiders

We present both an overview and a perspective of recent experimental advances and proposed new approaches to performing computation using memristors. A memristor is a 2-terminal passive component with a dynamic resistance depending on an internal parameter. We provide an brief historical introduction, as well as an overview over the physical mechanism that lead to memristive behavior. This review is meant to guide nonpractitioners in the field of memristive circuits and their connection to machine learning and neural computation.Comment: Perpective paper for MDPI Technologies; 43 page

Phase Noise Analyses and Measurements in the Hybrid Memristor-CMOS Phase-Locked Loop Design and Devices Beyond Bulk CMOS

Phase-locked loop (PLLs) has been widely used in analog or mixed-signal integrated circuits. Since there is an increasing market for low noise and high speed devices, PLLs are being employed in communications. In this dissertation, we investigated phase noise, tuning range, jitter, and power performances in different architectures of PLL designs. More energy efficient devices such as memristor, graphene, transition metal di-chalcogenide (TMDC) materials and their respective transistors are introduced in the design phase-locked loop. Subsequently, we modeled phase noise of a CMOS phase-locked loop from the superposition of noises from its building blocks which comprises of a voltage-controlled oscillator, loop filter, frequency divider, phase-frequency detector, and the auxiliary input reference clock. Similarly, a linear time-invariant model that has additive noise sources in frequency domain is used to analyze the phase noise. The modeled phase noise results are further compared with the corresponding phase-locked loop designs in different n-well CMOS processes. With the scaling of CMOS technology and the increase of the electrical field, the problem of short channel effects (SCE) has become dominant, which causes decay in subthreshold slope (SS) and positive and negative shifts in the threshold voltages of nMOS and pMOS transistors, respectively. Various devices are proposed to continue extending Moore\u27s law and the roadmap in semiconductor industry. We employed tunnel field effect transistor owing to its better performance in terms of SS, leakage current, power consumption etc. Applying an appropriate bias voltage to the gate-source region of TFET causes the valence band to align with the conduction band and injecting the charge carriers. Similarly, under reverse bias, the two bands are misaligned and there is no injection of carriers. We implemented graphene TFET and MoS2 in PLL design and the results show improvements in phase noise, jitter, tuning range, and frequency of operation. In addition, the power consumption is greatly reduced due to the low supply voltage of tunnel field effect transistor

Bifurcation and Chaos in Fractional-Order Systems

This book presents a collection of seven technical papers on fractional-order complex systems, especially chaotic systems with hidden attractors and symmetries, in the research front of the field, which will be beneficial for scientific researchers, graduate students, and technical professionals to study and apply. It is also suitable for teaching lectures and for seminars to use as a reference on related topics

Memristive System Based Image Processing Technology: A Review and Perspective

Copyright: © 2021 by the authors. As the acquisition, transmission, storage and conversion of images become more efficient, image data are increasing explosively. At the same time, the limitations of conventional computational processing systems based on the Von Neumann architecture continue to emerge, and thus, improving the efficiency of image processing has become a key issue that has bothered scholars working on images for a long time. Memristors with non-volatile, synapse-like, as well as integrated storage-and-computation properties can be used to build intelligent processing systems that are closer to the structure and function of biological brains. They are also of great significance when constructing new intelligent image processing systems with non-Von Neumann architecture and for achieving the integrated storage and computation of image data. Based on this, this paper analyses the mathematical models of memristors and discusses their applications in conventional image processing based on memristive systems as well as image processing based on memristive neural networks, to investigate the potential of memristive systems in image processing. In addition, recent advances and implications of memristive system-based image processing are presented comprehensively, and its development opportunities and challenges in different major areas are explored as well. By establishing a complete spectrum of image processing technologies based on memristive systems, this review attempts to provide a reference for future studies in the field, and it is hoped that scholars can promote its development through interdisciplinary academic exchanges and cooperationNational Natural Science Foundation of China (Grant U1909201, Grant 62001149); Natural Science Foundation of Zhejiang Province (Grant LQ21F010009)

Advanced Memristor Modeling

The investigation of new memory schemes, neural networks, computer systems and many other improved electronic devices is very important for future generations of electronic circuits and for their widespread application in all the areas of industry. Relatedly, the analysis of new efficient and advanced electronic elements and circuits is an essential field of highly developed electrical and electronic engineering. The resistance-switching phenomenon, observed in many amorphous oxides, has been investigated since 1970 and is promising for inclusion in technologies for constructing new electronic memories. It has been established that such oxide materials have the ability to change their conductance in accordance to the applied voltage and memorizing their state for a long time interval. Similar behavior was predicted for the memristor element by Leon Chua in 1971. The memristor was proposed in accordance with symmetry considerations and the relationships between the four basic electric quantities—electric current i, voltage v, charge q and flux linkage Ψ. The memristor is a passive one-port element, together with the capacitor, inductor and resistor. The Williams Hewlett Packard (HP) research group has made a link between resistive switching devices and the memristor proposed by Chua. In addition, a number of scientific papers related to memristors and memristor devices have been issued and several models for them have been proposed. The memristor is a highly nonlinear component. It relates the electric charge q and the flux linkage Ψ, expressed as a time integral of the voltage v. It has the important capability of remembering the electric charge passing through its cross-section, and its respective resistance, when the electrical signals are switched off. Due to its nano-scale dimensions, non-volatility and memorizing properties, the memristor is a sound potential candidate for applications in high-density computer memories, artificial neural networks, and many other electronic devices

Parameter extraction techniques for the analysis and modeling of resistive memories

A revision of the different numerical techniques employed to extract resistive switching (RS) and modeling parameters is presented. The set and reset voltages, commonly used for variability estimation, are calculated for different resistive memory technologies. The methodologies to extract the series resistance and the parameters linked to the charge-flux memristive modeling approach are also described. It is found that the obtained cycle-to-cycle (C2C) variability depends on the numerical technique used. This result is important, and it implies that when analyzing C2C variability, the extraction technique should be described to perform fair comparisons between different resistive memory technologies. In addition to the use of extensive experimental data for different types of resistive memories, we have also included kinetic Monte Carlo (kMC) simulations to study the formation and rupture events of the percolation paths that constitute the conductive filaments (CF) that allow resistive switching operation in filamentary unipolar and bipolar devices.Consejería de Conocimiento, Investigaci ́on y Universidad, Junta de Andalucía (Spain) and the FEDER program for the projects A.TIC.117.UGR18, B-TIC-624-UGR20 and IE2017-5414Ramón y Cajal grant No. RYC2020-030150-IFunding for open access charge: Universidad de Granada/CBU

ANALOG SIGNAL PROCESSING SOLUTIONS AND DESIGN OF MEMRISTOR-CMOS ANALOG CO-PROCESSOR FOR ACCELERATION OF HIGH-PERFORMANCE COMPUTING APPLICATIONS

Emerging applications in the field of machine vision, deep learning and scientific simulation require high computational speed and are run on platforms that are size, weight and power constrained. With the transistor scaling coming to an end, existing digital hardware architectures will not be able to meet these ever-increasing demands. Analog computation with its rich set of primitives and inherent parallel architecture can be faster, more efficient and compact for some of these applications. The major contribution of this work is to show that analog processing can be a viable solution to this problem. This is demonstrated in the three parts of the dissertation. In the first part of the dissertation, we demonstrate that analog processing can be used to solve the problem of stereo correspondence. Novel modifications to the algorithms are proposed which improves the computational speed and makes them efficiently implementable in analog hardware. The analog domain implementation provides further speedup in computation and has lower power consumption than a digital implementation. In the second part of the dissertation, a prototype of an analog processor was developed using commercially available off-the-shelf components. The focus was on providing experimental results that demonstrate functionality and to show that the performance of the prototype for low-level and mid-level image processing tasks is equivalent to a digital implementation. To demonstrate improvement in speed and power consumption, an integrated circuit design of the analog processor was proposed, and it was shown that such an analog processor would be faster than state-of-the-art digital and other analog processors. In the third part of the dissertation, a memristor-CMOS analog co-processor that can perform floating point vector matrix multiplication (VMM) is proposed. VMM computation underlies some of the major applications. To demonstrate the working of the analog co-processor at a system level, a new tool called PSpice Systems Option is used. It is shown that the analog co-processor has a superior performance when compared to the projected performances of digital and analog processors. Using the new tool, various application simulations for image processing and solution to partial differential equations are performed on the co-processor model

MOCAST 2021

The 10th International Conference on Modern Circuit and System Technologies on Electronics and Communications (MOCAST 2021) will take place in Thessaloniki, Greece, from July 5th to July 7th, 2021. The MOCAST technical program includes all aspects of circuit and system technologies, from modeling to design, verification, implementation, and application. This Special Issue presents extended versions of top-ranking papers in the conference. The topics of MOCAST include:Analog/RF and mixed signal circuits;Digital circuits and systems design;Nonlinear circuits and systems;Device and circuit modeling;High-performance embedded systems;Systems and applications;Sensors and systems;Machine learning and AI applications;Communication; Network systems;Power management;Imagers, MEMS, medical, and displays;Radiation front ends (nuclear and space application);Education in circuits, systems, and communications

Low Power Memory/Memristor Devices and Systems

This reprint focusses on achieving low-power computation using memristive devices. The topic was designed as a convenient reference point: it contains a mix of techniques starting from the fundamental manufacturing of memristive devices all the way to applications such as physically unclonable functions, and also covers perspectives on, e.g., in-memory computing, which is inextricably linked with emerging memory devices such as memristors. Finally, the reprint contains a few articles representing how other communities (from typical CMOS design to photonics) are fighting on their own fronts in the quest towards low-power computation, as a comparison with the memristor literature. We hope that readers will enjoy discovering the articles within